■乳腺癌的预后往往是不利的,强调早期转移风险检测和准确治疗预测的必要性。这项研究旨在开发一种新的多模式深度学习模型,使用术前数据来预测无病生存(DFS)。
■我们回顾性收集病理影像,来自中国癌症基因组图谱和一个独立机构的分子和临床数据。我们开发了一种用于DFS预测的基于病理基因多模式(DeepClinMed-PGM)的新型深度学习临床医学模型,将临床病理数据与分子见解相结合。患者包括训练组(n=741),内部验证队列(n=184),和外部测试队列(n=95)。
■将多模态数据集成到DeepClinMed-PGM模型中显着改善了接收器工作特性曲线(AUC)值下的面积。在训练组中,1-的AUC值,3-,和5年DFS预测增加到0.979,0.957和0.871,而在外部测试队列中,1-的值达到0.851、0.878和0.938,2-,和3年DFS预测,分别。DeepClinMed-PGM的强大判别能力在各个队列中始终很明显,包括训练队列[风险比(HR)0.027,95%置信区间(CI)0.0016-0.046,P<0.0001],内部验证队列(HR0.117,95%CI0.041-0.334,P<0.0001),和外部队列(HR0.061,95%CI0.017-0.218,P<0.0001)。此外,DeepClinMed-PGM模型显示三个队列中的C指数值为0.925、0.823和0.864,分别。
■这项研究介绍了一种乳腺癌预后的方法,整合成像、分子和临床数据以提高预测准确性,为个性化治疗策略提供承诺。
UNASSIGNED: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS).
UNASSIGNED: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based
Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95).
UNASSIGNED: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM\'s robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively.
UNASSIGNED: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.